Robustification hors ligne des lois de commande prédictives multivariables. Compromis entre robustesse en stabilité face à des incertitudes non structurées et performance nominale
Bibliographic record
Abstract
Cet article propose une méthodologie hors ligne de robustification de lois de commande prédictives multivariables, se basant sur une problématique d'optimisation convexe d'un paramètre de Youla-Kučera résolue par un formalisme d'inégalités linéaires matricielles. À partir d'une loi de commande stabilisante sous la forme d'un retour d'état et observateur, la démarche proposée consiste à synthétiser un paramètre de Youla-Kučera afin d'améliorer la robustesse en stabilité face à des incertitudes non structurées additives et/ou multiplicatives et d'assurer des performances nominales pour le rejet de perturbations, imposées sous la forme de gabarits temporels sur les sorties. Cette technique permet de gérer le compromis entre la robustesse en stabilité et les performances nominales et de réduire l'influence du couplage multivariable. Un exemple est proposé afin d'illustrer les résultats obtenus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".